PISA 2018 TÜRKİYE MATEMATİK OKURYAZARLIĞINI AÇIKLAYAN DEĞİŞKENLERİN CHAID ANALİZİ İLE İNCELENMESİ
Yıl 2023,
Cilt: 7 Sayı: 4, 1042 - 1063, 31.12.2023
Evrim Yalçın
,
Şerife Zeybekoğlu
,
Ayşe Bilicioğlu Güneş
,
Ömay Çokluk-bökeoglu
Öz
Bu çalışmanın amacı, Türk öğrencilerin Uluslararası Öğrenci Değerlendirme Programı (PISA) 2018 öğrenci anketine verdikleri yanıtlarla matematik okuryazarlığını açıklayan değişkenleri incelemektir. Çalışmanın konusu, eğitim sisteminde tespit edilecek eksikliklerin giderilerek gerekli önlemlerin alınabilmesi için bulgular sağlayacak olması yönüyle önemlidir. İlişkisel tarama modelinde gerçekleştirilen araştırmanın Türkiye örneklemini tabakalı örnekleme deseniyle seçilen 6890 öğrenci oluşturmaktadır. Araştırmanın örneklemini eksik değerleri içeren veriler çıkarıldıktan sonra kalan 5293 kişi oluşturmaktadır. Bu çalışmada veri analizi için veri madenciliği karar ağacı algoritmalarından biri olan Ki-kare Otomatik Etkileşim Tespiti (CHAID) yöntemi kullanılmıştır. Analiz sonucunda Türk öğrencilerin matematik okuryazarlığını en iyi açıklayan değişkenin “evdeki kitap sayısı” olduğu sonucuna ulaşılmıştır. Öğrencilerin Baba Eğitim Düzeyi, En Yüksek Ebeveyn Eğitim Seviyesi, Okulda ve Evde BİT Erişebilirliği, Matematik Dersi için Haftada Ayrılan Öğrenme Zamanı da matematik okuryazarlığını açıklayan diğer değişkenler olarak bulunmuştur.
Kaynakça
- Aksu, G.,& Güzeller, C. O. (2016). Classification of PISA 2012 mathematical literacy scores using decision-tree method: Turkey sampling. Education and Science, 41(185), 101-122.
- Aksu, G., Guzeller, C. E. M., & Eser, M. (2017). Analysis of maths literacy performances of students with Hierarchical Linear Modelling (HLM): The case of PISA 2012 Turkey. Education and Science, 42(191), 247- 266.
- Akyüz, G.,& Satıcı, K. (2013). Investigation of the factors affecting mathematics literacy using PISA 2003 results: Turkey and Hong Kong-China. Kastamonu Education Journal, 21(2), 503-522.
- Aslanoğlu, A. E. (2007). PIRLS 2001 Türkiye verilerine göre 4. sınıf öğrencilerinin okuduğunu anlama becerileriyle ilişkili faktörler (Order No. 234226) [Doctoral dissertation, Ankara University]. https://tez.yok.gov.tr/UlusalTezMerkezi/.
- Aydın, A., Sarıer, Y., & Uysal, Ş. (2012). The comparative assessment of the results of PISA mathematical literacy in terms of socio-economic and socio-cultural variables. Education and Science, 37(164), 20-30.
- Blau, D. (1999). The effect of income on child development. The Review of Economics and Statistics, 81(2), 261–276.
- Berry, M. J. A.,& Linoff, G. S. (2004). Data mining techniques: for marketing, sales, and customer relationship management. Wiley Publishing.
- Brown, G.,& Micklewright, J. (2004). Using international surveys of achievement and literacy: A view from the outside. Montreal, Rome: UNESCO Enstitute for Statistics.
- Cameron, S. V.,& Heckman, J. (2001). The dynamics of educational attainment for black, hispanic, and white males. Journal of Political Economy, 109(3), 455–499.
- Chang, T. S. (2011). A comparative study of artificial neural networks, and decision trees for digital game content stocks price prediction. Expert Systems with Applications, 38(12), 14846-14851.
- Chevalier, A.,& Lanot, G. (2002). The relative effect of family characteristics and financial situation on educational achievement. Education Economics, 10(2), 165–181.
- Çanakçı, O.,& Özdemir, A. Ş. (2015). Mathematics achievement and parent education level. Journal of Istanbul Aydın University, 7(25), 19-36.
- Çelen, F. K., Çelik, A., &Seferoğlu, S. S. (2011). Türk eğitim sistemi ve PISA sonuçları. Akademik Bilişim II, 2(4), 1-9.
- Demir, E.,& Parlak, B. (2012). Türkiye’de eğitim araştırmalarında kayıp veri sorunu. Journal of Measurement and Evaluation in Education and Psychology, 3(1), 230-241.
- Demir, E. (2015). Affective characteristics predicting 15-year-old students' mathematics literacy skills in Turkey. Ankara University Journal of Faculty of Educational Sciences (JFES), 48(2),165-184.
- Dibek M., İ, Yalçın S., & Yavuz H., Ç. (2016). Investigation on the relationships between information communication technology and mathematics literacy for Turkey Students. Journal of Ahi Evran University Kırşehir Faculty of Education, 17(3), 39-58.
- Doğan, İ. (2003). Investigation of the factors which are affecting the mUk yield in Holstein by CHAID analysis. Ankara Üniversitesi Veteriner Fakültesi Dergisi, 50(1), 65-70.
- Erikson, R.,& Jonsson, J. (1996). Introduction: Explaining class inequality in education: The Swedish test case. In R. Erikson, & J.O. Jonson (Eds.), Can education be equalized?: The Swedish case in comparative perspective (pp. 1–60). Boulder, CO: Westview Press.
- Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (7th Ed.). New York: McGraw-Hill.
- Ganzach, Y. (2000). Parents’ education, cognitive ability, educational expectations and educational attainment: Interactive effects. British Journal of Educational Psychology, 70(3), 419-441.
- Gilleece, L., Cosgrove, J., & Sofroniou, N. (2010). Equity in mathematics and science outcomes: Characteristics associated with high and low achievement on PISA 2006 in Ireland. International Journal of Science and Mathematics Education, 8(3), 475-496.
- Gürsakal S. (2012). An evaluation of PISA 2009 student achievement levels’ affecting factors. Suleyman Demirel University Journal of Faculty of Economics & Administrative Sciences, 17(1), 441-452.
- Haveman, R.,& Wolfe, B. (1995). The determinants of children’s attainment: A review of methods and findings. Journal of Economic Literature, 33(4), 1829–1878.
- Horner, S. B., Fireman, G. D., & Wang, E. W. (2010). The relation of student behavior, peer status, race, and gender to decisions about school discipline using CHAID decision trees and regression modeling. Journal of School Psychology, 48(2), 135-161.
- Hu, X., Gong, Y., Lai, C., & Leung, F. K. (2018). The relationship between ICT and student literacy in mathematics, reading, and science across 44 countries: A multilevel analysis. Computers & Education, 125, 1-13.
- Kahraman Ü.,&Çelik K. (2017). An analysis of 2012 PISA mathematics test scores in terms of some variables. Journal of Human Sciences, 14(4), 4797-4808.
- Karabay, E., Yıldırım, A., & Güler, G. (2015). The analysis of the relationship of PISA maths literacy with student and school characteristics by years with hierarchical linear models. Mehmet Akif Ersoy University Journal of Education Faculty, 1(36), 137-151.
- Karabay, E. (2013). Aile ve okul özelliklerinin PISA okuma becerileri, matematik ve fen okuryazarlığını yordama gücünün yıllara göre incelenmesi (Oder No. 349068) [Master’s Thesis, Gazi University]. https://tez.yok.gov.tr/UlusalTezMerkezi/.
- Kayri, M. (2014). Karar ağaçları. Karar Ağaçları Çalıştayı, Muş Alparslan Üniversitesi.
- Koğar, H. (2015). Examination of factors affecting PISA 2012 mathematical literacy through mediation model. Education and Science, 40(179), 45- 55.
- Keskin, G.,& Sezgin, B. (2009). Bir grup ergende akademik basarı durumuna etki eden etmenlerin belirlenmesi. Fırat Sağlık Hizmetleri Dergisi, 4(10), 3-18.
- Liu, A., Wei, Y., Xiu, Q., Yao, H., & Liu, J. (2023). How learning time allocation make sense on secondary school students’ academic performance: A Chinese evidence based on PISA 2018. Behavioural Sciences, 13(3), 237.
- Martins, L.,& Veiga, P. (2010). Do inequalities in parents’ education play an important role in PISA students’ mathematics achievement test score disparities? Economics of Education Review, 29(6), 1016-1033.
- MoNe (2019). PISA 2018 Türkiye ön raporu. Ankara: MoNe
- Mutluer, C.,& Büyükkıdık, S. (2017). Estimation on the mathematics literacy with logistic regression according to PISA 2012 data. Marmara University Atatürk Education Faculty Journal of Educational Sciences, 46(46), 97-112.
- OECD (2019a). PISA 2018 assessment and analytical framework. Paris: OECD Publishing.
- OECD (2019b). PISA 2018 results volume I: What students know and can do. Paris: OECD Publishing.
- Oğuzlar, A. (2004). CART Analizi ile hanehalkı işgücü anketi sonuçlarının özetlenmesi. Atatürk Üniversitesi İkdisadi ve İdari Bilimler Dergisi, 18(3-4), 79-90.
- Özer, Y.,&Anıl, D. (2011). Examining the factors affecting students’ science and mathematics achievement with structural equation modeling. Hacettepe University Journal of Education,41, 313-324.
- Shea, J. (2000). Does parents’ money matter?Journal of public Economics, 77(2), 155-184.
- Silahtaroğlu, G. (2013). Veri madenciliği kavram ve algoritmaları. İstanbul: Papatya Yayınevi.
- Şahin, M. G.,& Yıldırım, Y. (2016). The examination of the variables affecting mathematics behavior and mathematics literacy by multi-group hybrid model in the sample of PISA 2012 Turkey. Education and Science, 41(187), 181- 198.
- Teachman, J. (1987). Family background, educational resources and educational attainment. American Sociological Review, 52, 548–557.
- Turkan, A., Selman, U., &Alci, B. (2015). An analysis of 2012 PISA mathematics test scores in terms of some variables. Ege Journal of Education, 16(2), 358-372.
- Uysal, E.,&Yenilmez, K. (2011).The mathematics literacy level of eighth grade students. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 12(2), 1-15.
- Usta, H. G. (2014). PISA 2003 ve PISA 2012 matematik okuryazarlığı üzerine uluslararası bir karşılaştırma: Türkiye ve Finlandiya (Order No: 370331) ([Doctoral dissertation, Ankara University]. https://tez.yok.gov.tr/UlusalTezMerkezi/.
- Zeybekoğlu, Ş.,& Koğar, Hakan. (2022). Investigation of Variables Explaining Science Literacy in PISA 2015 Turkey Sample. Journal of Measurement and Evaluation in Education and Psychology, 13(2), 145-163.
THE EXAMINATION OF VARIABLES EXPLAINING MATHEMATICS LITERACY BY CHAID ANALYSIS: PISA 2018 TURKEY
Yıl 2023,
Cilt: 7 Sayı: 4, 1042 - 1063, 31.12.2023
Evrim Yalçın
,
Şerife Zeybekoğlu
,
Ayşe Bilicioğlu Güneş
,
Ömay Çokluk-bökeoglu
Öz
This study aims to investigate the variables explaining mathematics literacy of Turkish students who attended at Programme for International Student Assessment (PISA). The topic of the study is at utmost importance due to the potential findings that will identify the variables influencing mathematical literacy in our education system and generate recommendations aimed at addressing shortcomings.This study utilizes the answers given to the PISA student questionnaire, which are analysed by correlational survey design.Stratified sampling design is used in the selection of 6890 students in Turkish sample. After excluding the missing data, the sample of the study consists of 5293 participants. Chi-squared Automatic Interaction Detection (CHAID) method, which is one of the data mining decision tree algorithms, is used for data analysis. According to the results of the study, the most important variable explaining Turkish students’ mathematic literacy is the number of the books at home. Father’s education level, highest parental education level, accessibility of ICT both at home and school, and time allocated per week to study mathematics are other variables explaining mathematics literacy.
Kaynakça
- Aksu, G.,& Güzeller, C. O. (2016). Classification of PISA 2012 mathematical literacy scores using decision-tree method: Turkey sampling. Education and Science, 41(185), 101-122.
- Aksu, G., Guzeller, C. E. M., & Eser, M. (2017). Analysis of maths literacy performances of students with Hierarchical Linear Modelling (HLM): The case of PISA 2012 Turkey. Education and Science, 42(191), 247- 266.
- Akyüz, G.,& Satıcı, K. (2013). Investigation of the factors affecting mathematics literacy using PISA 2003 results: Turkey and Hong Kong-China. Kastamonu Education Journal, 21(2), 503-522.
- Aslanoğlu, A. E. (2007). PIRLS 2001 Türkiye verilerine göre 4. sınıf öğrencilerinin okuduğunu anlama becerileriyle ilişkili faktörler (Order No. 234226) [Doctoral dissertation, Ankara University]. https://tez.yok.gov.tr/UlusalTezMerkezi/.
- Aydın, A., Sarıer, Y., & Uysal, Ş. (2012). The comparative assessment of the results of PISA mathematical literacy in terms of socio-economic and socio-cultural variables. Education and Science, 37(164), 20-30.
- Blau, D. (1999). The effect of income on child development. The Review of Economics and Statistics, 81(2), 261–276.
- Berry, M. J. A.,& Linoff, G. S. (2004). Data mining techniques: for marketing, sales, and customer relationship management. Wiley Publishing.
- Brown, G.,& Micklewright, J. (2004). Using international surveys of achievement and literacy: A view from the outside. Montreal, Rome: UNESCO Enstitute for Statistics.
- Cameron, S. V.,& Heckman, J. (2001). The dynamics of educational attainment for black, hispanic, and white males. Journal of Political Economy, 109(3), 455–499.
- Chang, T. S. (2011). A comparative study of artificial neural networks, and decision trees for digital game content stocks price prediction. Expert Systems with Applications, 38(12), 14846-14851.
- Chevalier, A.,& Lanot, G. (2002). The relative effect of family characteristics and financial situation on educational achievement. Education Economics, 10(2), 165–181.
- Çanakçı, O.,& Özdemir, A. Ş. (2015). Mathematics achievement and parent education level. Journal of Istanbul Aydın University, 7(25), 19-36.
- Çelen, F. K., Çelik, A., &Seferoğlu, S. S. (2011). Türk eğitim sistemi ve PISA sonuçları. Akademik Bilişim II, 2(4), 1-9.
- Demir, E.,& Parlak, B. (2012). Türkiye’de eğitim araştırmalarında kayıp veri sorunu. Journal of Measurement and Evaluation in Education and Psychology, 3(1), 230-241.
- Demir, E. (2015). Affective characteristics predicting 15-year-old students' mathematics literacy skills in Turkey. Ankara University Journal of Faculty of Educational Sciences (JFES), 48(2),165-184.
- Dibek M., İ, Yalçın S., & Yavuz H., Ç. (2016). Investigation on the relationships between information communication technology and mathematics literacy for Turkey Students. Journal of Ahi Evran University Kırşehir Faculty of Education, 17(3), 39-58.
- Doğan, İ. (2003). Investigation of the factors which are affecting the mUk yield in Holstein by CHAID analysis. Ankara Üniversitesi Veteriner Fakültesi Dergisi, 50(1), 65-70.
- Erikson, R.,& Jonsson, J. (1996). Introduction: Explaining class inequality in education: The Swedish test case. In R. Erikson, & J.O. Jonson (Eds.), Can education be equalized?: The Swedish case in comparative perspective (pp. 1–60). Boulder, CO: Westview Press.
- Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (7th Ed.). New York: McGraw-Hill.
- Ganzach, Y. (2000). Parents’ education, cognitive ability, educational expectations and educational attainment: Interactive effects. British Journal of Educational Psychology, 70(3), 419-441.
- Gilleece, L., Cosgrove, J., & Sofroniou, N. (2010). Equity in mathematics and science outcomes: Characteristics associated with high and low achievement on PISA 2006 in Ireland. International Journal of Science and Mathematics Education, 8(3), 475-496.
- Gürsakal S. (2012). An evaluation of PISA 2009 student achievement levels’ affecting factors. Suleyman Demirel University Journal of Faculty of Economics & Administrative Sciences, 17(1), 441-452.
- Haveman, R.,& Wolfe, B. (1995). The determinants of children’s attainment: A review of methods and findings. Journal of Economic Literature, 33(4), 1829–1878.
- Horner, S. B., Fireman, G. D., & Wang, E. W. (2010). The relation of student behavior, peer status, race, and gender to decisions about school discipline using CHAID decision trees and regression modeling. Journal of School Psychology, 48(2), 135-161.
- Hu, X., Gong, Y., Lai, C., & Leung, F. K. (2018). The relationship between ICT and student literacy in mathematics, reading, and science across 44 countries: A multilevel analysis. Computers & Education, 125, 1-13.
- Kahraman Ü.,&Çelik K. (2017). An analysis of 2012 PISA mathematics test scores in terms of some variables. Journal of Human Sciences, 14(4), 4797-4808.
- Karabay, E., Yıldırım, A., & Güler, G. (2015). The analysis of the relationship of PISA maths literacy with student and school characteristics by years with hierarchical linear models. Mehmet Akif Ersoy University Journal of Education Faculty, 1(36), 137-151.
- Karabay, E. (2013). Aile ve okul özelliklerinin PISA okuma becerileri, matematik ve fen okuryazarlığını yordama gücünün yıllara göre incelenmesi (Oder No. 349068) [Master’s Thesis, Gazi University]. https://tez.yok.gov.tr/UlusalTezMerkezi/.
- Kayri, M. (2014). Karar ağaçları. Karar Ağaçları Çalıştayı, Muş Alparslan Üniversitesi.
- Koğar, H. (2015). Examination of factors affecting PISA 2012 mathematical literacy through mediation model. Education and Science, 40(179), 45- 55.
- Keskin, G.,& Sezgin, B. (2009). Bir grup ergende akademik basarı durumuna etki eden etmenlerin belirlenmesi. Fırat Sağlık Hizmetleri Dergisi, 4(10), 3-18.
- Liu, A., Wei, Y., Xiu, Q., Yao, H., & Liu, J. (2023). How learning time allocation make sense on secondary school students’ academic performance: A Chinese evidence based on PISA 2018. Behavioural Sciences, 13(3), 237.
- Martins, L.,& Veiga, P. (2010). Do inequalities in parents’ education play an important role in PISA students’ mathematics achievement test score disparities? Economics of Education Review, 29(6), 1016-1033.
- MoNe (2019). PISA 2018 Türkiye ön raporu. Ankara: MoNe
- Mutluer, C.,& Büyükkıdık, S. (2017). Estimation on the mathematics literacy with logistic regression according to PISA 2012 data. Marmara University Atatürk Education Faculty Journal of Educational Sciences, 46(46), 97-112.
- OECD (2019a). PISA 2018 assessment and analytical framework. Paris: OECD Publishing.
- OECD (2019b). PISA 2018 results volume I: What students know and can do. Paris: OECD Publishing.
- Oğuzlar, A. (2004). CART Analizi ile hanehalkı işgücü anketi sonuçlarının özetlenmesi. Atatürk Üniversitesi İkdisadi ve İdari Bilimler Dergisi, 18(3-4), 79-90.
- Özer, Y.,&Anıl, D. (2011). Examining the factors affecting students’ science and mathematics achievement with structural equation modeling. Hacettepe University Journal of Education,41, 313-324.
- Shea, J. (2000). Does parents’ money matter?Journal of public Economics, 77(2), 155-184.
- Silahtaroğlu, G. (2013). Veri madenciliği kavram ve algoritmaları. İstanbul: Papatya Yayınevi.
- Şahin, M. G.,& Yıldırım, Y. (2016). The examination of the variables affecting mathematics behavior and mathematics literacy by multi-group hybrid model in the sample of PISA 2012 Turkey. Education and Science, 41(187), 181- 198.
- Teachman, J. (1987). Family background, educational resources and educational attainment. American Sociological Review, 52, 548–557.
- Turkan, A., Selman, U., &Alci, B. (2015). An analysis of 2012 PISA mathematics test scores in terms of some variables. Ege Journal of Education, 16(2), 358-372.
- Uysal, E.,&Yenilmez, K. (2011).The mathematics literacy level of eighth grade students. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 12(2), 1-15.
- Usta, H. G. (2014). PISA 2003 ve PISA 2012 matematik okuryazarlığı üzerine uluslararası bir karşılaştırma: Türkiye ve Finlandiya (Order No: 370331) ([Doctoral dissertation, Ankara University]. https://tez.yok.gov.tr/UlusalTezMerkezi/.
- Zeybekoğlu, Ş.,& Koğar, Hakan. (2022). Investigation of Variables Explaining Science Literacy in PISA 2015 Turkey Sample. Journal of Measurement and Evaluation in Education and Psychology, 13(2), 145-163.